Ye Liguo, Gu Lingui, Zheng Zhiyao, Zhang Xin, Xing Hao, Guo Xiaopeng, Chen Wenlin, Wang Yaning, Wang Yuekun, Liang Tingyu, Wang Hai, Li Yilin, Jin Shanmu, Shi Yixin, Liu Delin, Yang Tianrui, Liu Qianshu, Deng Congcong, Wang Yu, Ma Wenbin
Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Front Neurol. 2023 May 19;14:1179761. doi: 10.3389/fneur.2023.1179761. eCollection 2023.
The World Health Organization (WHO) CNS5 classification system highlights the significance of molecular biomarkers in providing meaningful prognostic and therapeutic information for gliomas. However, predicting individual patient survival remains challenging due to the lack of integrated quantitative assessment tools. In this study, we aimed to design a WHO CNS5-related risk signature to predict the overall survival (OS) rate of glioma patients using machine learning algorithms.
We extracted data from patients who underwent an operation for histopathologically confirmed glioma from our hospital database (2011-2022) and split them into a training and hold-out test set in a 7/3 ratio. We used biological markers related to WHO CNS5, clinical data (age, sex, and WHO grade), and prognosis follow-up information to identify prognostic factors and construct a predictive dynamic nomograph to predict the survival rate of glioma patients using 4 kinds machine learning algorithms (RF, SVM, XGB, and GLM).
A total of 198 patients with complete WHO5 molecular data and follow-up information were included in the study. The median OS time of all patients was 29.77 [95% confidence interval (CI): 21.19-38.34] months. Age, FGFR2, IDH1, CDK4, CDK6, KIT, and CDKN2A were considered vital indicators related to the prognosis and OS time of glioma. To better predict the prognosis of glioma patients, we constructed a WHO5-related risk signature and nomogram. The AUC values of the ROC curves of the nomogram for predicting the 1, 3, and 5-year OS were 0.849, 0.835, and 0.821 in training set, and, 0.844, 0.943, and 0.959 in validation set. The calibration plot confirmed the reliability of the nomogram, and the c-index was 0.742 in training set and 0.775 in validation set. Additionally, our nomogram showed a superior net benefit across a broader scale of threshold probabilities in decision curve analysis. Therefore, we selected it as the backend for the online survival prediction tool (Glioma Survival Calculator, https://who5pumch.shinyapps.io/DynNomapp/), which can calculate the survival probability for a specific time of the patients.
An online prognosis predictor based on WHO5-related biomarkers was constructed. This therapeutically promising tool may increase the precision of forecast therapy outcomes and assess prognosis.
世界卫生组织（WHO）CNS5分类系统强调了分子生物标志物在为胶质瘤提供有意义的预后和治疗信息方面的重要性。然而，由于缺乏综合定量评估工具，预测个体患者的生存率仍然具有挑战性。在本研究中，我们旨在设计一种与WHO CNS5相关的风险特征，使用机器学习算法预测胶质瘤患者的总生存率（OS）。
我们从我院数据库（2011 - 2022年）中提取了经组织病理学确诊为胶质瘤并接受手术的患者数据，并以7/3的比例将其分为训练集和保留测试集。我们使用与WHO CNS5相关的生物标志物、临床数据（年龄、性别和WHO分级）以及预后随访信息来确定预后因素，并使用4种机器学习算法（随机森林（RF）、支持向量机（SVM）、极端梯度提升（XGB）和广义线性模型（GLM））构建预测动态列线图以预测胶质瘤患者的生存率。
本研究共纳入198例具有完整WHO5分子数据和随访信息的患者。所有患者的中位OS时间为29.77[95%置信区间（CI）：21.19 - 38.34]个月。年龄、FGFR2、IDH1、CDK4、CDK6、KIT和CDKN2A被认为是与胶质瘤预后和OS时间相关的重要指标。为了更好地预测胶质瘤患者的预后，我们构建了一个与WHO5相关的风险特征和列线图。列线图预测1年、3年和5年OS的ROC曲线的AUC值在训练集中分别为0.849、0.835和0.821，在验证集中分别为0.844、0.943和0.959。校准图证实了列线图的可靠性，训练集的c指数为0.742，验证集的c指数为0.775。此外，在决策曲线分析中，我们的列线图在更广泛的阈值概率范围内显示出更高的净效益。因此，我们选择它作为在线生存预测工具（胶质瘤生存计算器，https://who5pumch.shinyapps.io/DynNomapp/）的后端，该工具可以计算患者特定时间的生存概率。
构建了一种基于与WHO5相关生物标志物 的在线预后预测工具。这个具有治疗前景的工具可能会提高预测治疗结果和评估预后的准确性。
